Mobile edge computing (MEC) is currently viewed as a prime emerging technology for the next generation mobile networks and Internet of Things (IoT). It allows to extend conventional cloud computing (CC) at the network edge to support real-time compute-intensive and high-bandwidth demanding mobile, IoT, and Big Data applications.
The architecture of MEC network contains an intermediate MEC layer between the cloud and end-users’ devices formed by distributed MEC servers with storage, computing, communication, and routing functions. MEC servers can be placed in stationary, quasi-stationary, and mobile locations, e.g., at the base stations (BSs), Wi-Fi access points (APs), unmanned aerial vehicles (UAVs), or users' mobile terminals.
Since MEC is still a very new concept, there are many open research problems that must be resolved to enable the deployment of MEC systems in real-world scenarios. Accordingly, this project aims to explore the design and implementation of large-scale MEC networks formed by numerous MEC servers that can be maintained by different mobile network operators (MNOs) and service providers (SPs) by addressing the following critical aspects:
1) resource management to guarantee the connectivity and required quality of service (QoS) for end-users given a decentralized nature of the network nodes controlled by different MNOs or SPs and the presence of incomplete (i.e., partially-observable) information about the network state;
2) content distribution and computational offloading to support efficient delivery of content and offloading of users’ computing tasks in a highly stochastic MEC environment with dynamic computing, caching, and spectrum resources and unstable wireless connections.
NSFC Research Grant (2020-2022): Complex Large-Scale Mobile Edge Computing Heterogeneous Networks
 A. Asheralieva and D. Niyato, “Bayesian Reinforcement Learning and Bayesian Deep Learning for Blockchains with Mobile Edge Computing,” IEEE Transactions on Cognitive Communications and Networking, Early Access, May 2020.
 A. Asheralieva and D. Niyato, “Combining Contract Theory and Lyapunov Optimization for Content Sharing With Edge Caching and Device-to-Device Communications,” IEEE/ACM Transactions on Networking, vol. 28, no. 3, pp. 1213-1226, June 2020.
 A. Asheralieva and D. Niyato, “Distributed Dynamic Resource Management and Pricing in the IoT Systems with Blockchain-as-a-Service and UAV-Enabled Mobile Edge Computing,” IEEE Internet of Things Journal, vol. 7, no. 3, pp. 1974-1993, March 2020.
 A. Asheralieva and D. Niyato, “Learning-Based Mobile Edge Computing Resource Management to Support Public Blockchain Networks,” IEEE Transactions on Mobile Computing, Early Access, Dec. 2019.
 A. Asheralieva and D. Niyato, “Hierarchical Game-Theoretic and Reinforcement Learning Framework for Computational Offloading in UAV-Enabled Mobile Edge Computing Networks with Multiple Service Providers,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8753-8769, Oct. 2019.
 A. Asheralieva and D. Niyato, “Game Theory and Lyapunov Optimization for Cloud-Based Content Delivery Networks with Device-to-Device and UAV-Enabled Caching,” IEEE Transactions on Vehicular Technology, vol. 68, no. 10, pp. 10094-10110, Oct. 2019.
 A. Asheralieva, “Optimal Computational Offloading and Content Caching in Wireless Heterogeneous Mobile Edge Computing Systems With Hopfield Neural Networks,” IEEE Transactions on Emerging Topics in Computational Intelligence, Early Access, Feb. 2019.
Dusit (Tao) Niyato, Ph.D., IEEE Fellow, Professor
School of Computer Science and Engineering (SCSE) and School of Physical and Mathematical Sciences (SPMS)
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